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Identification of online reviews helpfulness using Neural Networks

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Identification of online reviews helpfulness using Neural Networks

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dc.contributor.author Olmedilla, María es_ES
dc.contributor.author Martínez Torres, Mª del Rocío es_ES
dc.contributor.author Toral, Sergio L. es_ES
dc.date.accessioned 2020-07-28T07:20:00Z
dc.date.available 2020-07-28T07:20:00Z
dc.date.issued 2020-07-10
dc.identifier.isbn 9788490488324
dc.identifier.uri http://hdl.handle.net/10251/148759
dc.description.abstract [EN] During the last decade, research has shown that identifying helpful reviews from a big amount of user-generated review data has been a trending topic. This study proposes a classification system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can early identify whether an online review is helpful, fair or not helpful with 80% of accuracy. After using the neuronal encoding, a cluster analysis of the helpful and not helpful was made. The results reveal that the most significant words and documents for helpful reviews clusters describe cars and their characteristics. Whereas not helpful reviews clusters express details on car-related shops/companies in general. es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Web data es_ES
dc.subject Internet data es_ES
dc.subject Big data es_ES
dc.subject Qca es_ES
dc.subject Pls es_ES
dc.subject Sem es_ES
dc.subject Conference es_ES
dc.subject Helpfulness es_ES
dc.subject Online reviews es_ES
dc.subject Convolutional Neural Networks es_ES
dc.subject Predictions es_ES
dc.subject Classification es_ES
dc.title Identification of online reviews helpfulness using Neural Networks es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Olmedilla, M.; Martínez Torres, MDR.; Toral, SL. (2020). Identification of online reviews helpfulness using Neural Networks. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/148759 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics es_ES
dc.relation.conferencedate Julio 08-09,2020 es_ES
dc.relation.conferenceplace Valencia, Spain es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/CARMA/CARMA2020/paper/view/11502 es_ES
dc.description.upvformatpinicio 336
dc.description.upvformatpfin 336
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela OCS\11502 es_ES


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